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Remote sensing

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Articles 91 - 120 of 717

Full-Text Articles in Physical Sciences and Mathematics

Mapping Kirtland’S Warbler (Setophaga Kirtlandii) Stationary Non-Breeding Habitat: Characterizing Land Cover Within The South-Central Bahamas And Evaluating The Impacts Of Sea Level Rise, Cole Anthony Scrivner Jan 2025

Mapping Kirtland’S Warbler (Setophaga Kirtlandii) Stationary Non-Breeding Habitat: Characterizing Land Cover Within The South-Central Bahamas And Evaluating The Impacts Of Sea Level Rise, Cole Anthony Scrivner

Antioch University Dissertations & Theses

Understanding the spatial distribution of current and future habitat for the Kirtland’s Warbler (Setophaga kirtlandii) and other threatened species is critical for guiding conservation planning in The Bahamas. Our study mapped land cover and impacts of sea level rise across the south-central Bahamian islands, as an initial step to determine suitable areas for Kirtland’s Warblers in the region. We used a Random Forest (RF) classification of multispectral satellite image bands and indices from Landsat and Sentinel-2 image composites to predict land cover. The classification identified tropical dry forest communities (broadleaved, semi-evergreen trees and shrubs known as coppice) as …

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Applications Of Uav In Landslide Research: A Review, Boneng Chen, Jeremy Maurer, Weibing Gong Jan 2025

Applications Of Uav In Landslide Research: A Review, Boneng Chen, Jeremy Maurer, Weibing Gong

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Uncrewed aerial vehicles (UAVs), commonly known as drones, have gained significant popularity in landslide research due to their operational flexibility, near real-time planning capabilities, cost-effectiveness, high-resolution data outputs, and enhanced safety. This review aims to provide an analysis of UAV-based landslide investigation methodologies and their applications. It begins with an examination of UAV platforms and UAV-equipped sensors employed in landslide research, evaluating their advantages and limitations to guide appropriate platform and sensor selection. The review then explores UAV applications across three primary landslide investigation domains: landslide mapping, landslide monitoring, and landslide hazard assessment, highlighting some representative achievements in each domain. …

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Earth Observations Reveal Impacts Of Climate Variability On Maize Cropping Systems In Sub-Saharan Africa, Adomas Liepa, Michael Thiel, Hannes Taubenböck, Doris Klein, Ingolf Steffan-Dewenter, Marcell K. Peters, Sarah Schönbrodt-Stitt, Insa Otte, Tobias Landmann, Zeyaur R. Khan, Michael Ochieng Obondo, Frank Chidawanyika, Emily A. Martin, Tobias Ullmann Jan 2025

Earth Observations Reveal Impacts Of Climate Variability On Maize Cropping Systems In Sub-Saharan Africa, Adomas Liepa, Michael Thiel, Hannes Taubenböck, Doris Klein, Ingolf Steffan-Dewenter, Marcell K. Peters, Sarah Schönbrodt-Stitt, Insa Otte, Tobias Landmann, Zeyaur R. Khan, Michael Ochieng Obondo, Frank Chidawanyika, Emily A. Martin, Tobias Ullmann

All Peer-Reviewed Publications

In Kenya, climate variability and change threaten smallholder, rainfed farms, with crop failures, yield reductions, and pest infestations. Efficient agroecological strategies, such as Push-Pull intercropping, offer documented benefits including pest control, improved soil fertility, and water conservation compared to traditional maize monocropping. To date, no studies exist comparing traditional maize monocropping and Push-Pull intercropping using earth observation tools over several growing seasons in East Africa. Our research addresses this by harmonizing Landsat 7, 8, 9 with Sentinel-2 remote sensing time series from 2016 to 2023. Phenological metrics of 15 growing seasons are extracted based on a threshold method using the …

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Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu Jan 2025

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

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Bridging The Gap Between Plot-Level And Landscape-Scale Analysis For Wildfire Risk Assessment, Vanessa Leigh Niemczyk Jan 2025

Bridging The Gap Between Plot-Level And Landscape-Scale Analysis For Wildfire Risk Assessment, Vanessa Leigh Niemczyk

Graduate Student Theses, Dissertations, & Professional Papers

Remote sensing technology has advanced greatly over the past couple of decades proving its ability to aid in wildfire risk assessment and improve our understanding of forest structure and fuel inventory across the landscape. While some aerial and satellite sensors perform better than others, they all have a common weakness, their reduced ability to capture understory fuels with high detail. Terrestrial laser scanning is an emerging solution due to its understory perspective. This research leverages the beneficial aspects of both terrestrial laser scanning and various aerial- or satellite-based remote sensing platforms (aerial laser scanning, digital aerial photogrammetry, and Sentinel-2) to …

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Evaluating The Causes Of Land Subsidence On The U.S. Mid-Atlantic Coast Measured With Insar, Jordan Leigh Diprima Jan 2025

Evaluating The Causes Of Land Subsidence On The U.S. Mid-Atlantic Coast Measured With Insar, Jordan Leigh Diprima

Honors Theses and Capstones

Land subsidence is a frequently overlooked geologic hazard that is caused by natural processes and anthropogenic stressors. The goal of this study is to quantify vertical land motion (VLM) on Long Island, New York and Virginia’s Eastern Shore and evaluate the potential causes of subsidence. The causes considered in this work are glacial isostatic adjustment, groundwater extraction, infrastructure loading, and land cover. This study utilizes interferometric synthetic aperture radar (InSAR) satellite data from Sentinel-1 to calculate linear VLM trends from 2017 to 2023. Datasets for each hypothesis were qualitatively compared to VLM data. Subsidence rates in both regions were found …

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An Integrated Ecosystem Monitoring Technology For Coal Mining Subsidence Areas And Its Application In The Shendong Mining Area, Cheng Yang, Liu Wei, Zhang Na, Li Guanjie, Liu Kai, Zhang Chengye, Li Jun Dec 2024

An Integrated Ecosystem Monitoring Technology For Coal Mining Subsidence Areas And Its Application In The Shendong Mining Area, Cheng Yang, Liu Wei, Zhang Na, Li Guanjie, Liu Kai, Zhang Chengye, Li Jun

Coal Geology & Exploration

Background The ecosystem monitoring of arid and semi-arid coal mining subsidence areas acts as a significant prerequisite for regional ecosystem conservation and management, holding critical significance for accelerating green mine construction. Methods Based on the Chinese government's regulatory requirements for mine ecosystems, this study analyzed the difficulties in ecosystem monitoring in the coal mining subsidence area of the Shendong mining area (also referred to as the Shendong coal mining subsidence area). By detailing the integrated coal-rock-water-soil-air-vegetation-carbon monitoring technology system, this study determined the factors, principal methods, and technology roadmap for the integrated ecosystem monitoring in the Shendong coal mining subsidence …

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Estimating Medium-Term Regional Monthly Economic Activity Reductions During The Covid-19 Pandemic Using Nighttime Light Data, Ma Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano Dec 2024

Estimating Medium-Term Regional Monthly Economic Activity Reductions During The Covid-19 Pandemic Using Nighttime Light Data, Ma Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano

SOSE Affiliate: Manila Observatory

Economic impact estimates of the initial lockdowns due to the COVID-19 pandemic showed a significant reduction in economic activities globally. However, the succeeding impacts and their spatiotemporal distribution within countries remain unknown. Studies showed that nighttime light data (NTL) has effectively revealed the spatiotemporal dimensions of the economic effects of COVID-19. Thus, this study used NTL data to determine the medium-term regional monthly economic impacts of the pandemic in the Philippines in terms of the Economic Activity Reduction (EAR) index. We generated a spatial error model, regressing pre-pandemic NTL on mean temperature, maximum rainfall, and built-up area. This model explained …

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Landslide Inventory And Unstable Slope Monitoring Along Highways In Eastern Tennessee, Robert Mcsweeney Dec 2024

Landslide Inventory And Unstable Slope Monitoring Along Highways In Eastern Tennessee, Robert Mcsweeney

Electronic Theses and Dissertations

This research introduces an unstable slope management program (USMP) for Tennessee based on federal slope management standards, along with improved methods for landslide monitoring with unmanned aerial systems (UAS) lidar and photogrammetry. In mountainous regions, monitoring slope hazards is a critical function of transportation management. A mobile field assessment form created with Survey123 was used to collect 22 unstable slope ratings in eastern Tennessee. Location points were appended with photographs, notes, and site information. Landslide scores ranged from 325 (Fair) to 1005 (Poor). UAS monitoring of a slow-moving soil landslide along I-40 near Rockwood, TN, produced high-resolution lidar and photogrammetry …

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Machine Learning Methods For Quantification Of Glacier Variations Through Satellite Imagery, Robert D. Breininger Dec 2024

Machine Learning Methods For Quantification Of Glacier Variations Through Satellite Imagery, Robert D. Breininger

Theses and Dissertations

Glaciers around the world have experienced a trend of recession within the past century. Quantification of glacier variations using satellite imagery is of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. The potential methods to quantify glacier variations with increasing complexity include detecting the terminus location, quantifying the glacier surface area, and measuring glacier volume. Although there are methods in literature designed purposefully for glacier area segmentation that have achieved acceptable results, they are often localized to the region where their training data were acquired and further rely on training sets …

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On Spatiotemporal Trends In Meter-Scale Gris Surface Roughness And The Development Of An On-Ice Laser Distance Meter, Jamie C. Good Nov 2024

On Spatiotemporal Trends In Meter-Scale Gris Surface Roughness And The Development Of An On-Ice Laser Distance Meter, Jamie C. Good

Dartmouth College Master’s Theses

Surface roughness is a critical component of the energy and mass balance of the Greenland Ice Sheet (GrIS). However, roughness is often oversimplified in predictive models due to its inherent scale dependency. Accurate quantification of roughness, including its recent changes and driving factors, is critically important for understanding and predicting GrIS surface dynamics. Using multi- and single-scale methods of roughness analysis, I assess spatiotemporal trends in GrIS meter-scale surface roughness from 2009 to 2019 with Operation IceBridge’s Airborne Topographic Mapper ILATM2 product. Additionally, with data from on-ice automated weather stations, I employ machine learning techniques to identify primary climatic controls …

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Sward Production Estimated By Spectral Reflectance, G Nagy, V Zilinyi Aug 2024

Sward Production Estimated By Spectral Reflectance, G Nagy, V Zilinyi

IGC Proceedings (1977-2023)

A non-destructive spectral reflectance method was used for the measurement of aerial biomass yields of different grasslands. Vegetation indices {VI) were calculated from the green, red and near-infrared light reflectance. These indices were compared with actual grass yields cut just after the reflectance measurement. The correlation coefficient between actual yields and VI was significant (r=0.969,n=24). This non-destructive technique is therefore a reliable tool for the estimation of standing biomass of grasslands.

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Application Of Neural Networks To The Extraction Of Various Types Of Grasslands In Japan Using Landsat Thematic Mapper Data, Mikinori Tsuiki, Shigeo Takahashi, Toshiki Oku Aug 2024

Application Of Neural Networks To The Extraction Of Various Types Of Grasslands In Japan Using Landsat Thematic Mapper Data, Mikinori Tsuiki, Shigeo Takahashi, Toshiki Oku

IGC Proceedings (1977-2023)

A neural network was applied to the extraction of various types of grasslands using Landsat Thematic Mapper (TM) data. Training fields contained 12 classes (water, paddy field, farmland, sands and rocks, urban area, coniferous forest, deciduous forest, golf course, Sasa­type grassland, Miscanthus type grassland, meadow before culling and meadow after cutting). Classification performance using the neural network was 99.4%, which was 2.4% higher than that obtained using the maximum likelihood method. For all types of grasslands, classification performance was 99.8%. The results of the classification area obtained using the neural network and the maximum likelihood method resembled each other. …

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Key Largo Mangrove Population Monitoring: A Remote Sensing Analysis And Classification Methodology Review, David Lackajs Aug 2024

Key Largo Mangrove Population Monitoring: A Remote Sensing Analysis And Classification Methodology Review, David Lackajs

Geography and the Environment: Graduate Student Capstones

Mangrove forests are some of the world's most bio-diverse habitats, providing essential services to the surrounding coasts. Removal of these habitats has a devastating impact on the ecosystems within them. The Florida Keys are some of the last areas in the United States with extensive mangrove populations. One specific area, John Pennekamp Coral Reef State Park in Key Largo, has been under state protection since 1959. For that reason, mangrove forest habitats there are less fragmented. This study uses remotely sensed imagery to quantify and analyze mangrove populations in this area using two methods: sub-pixel analysis and supervised classification. The …

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Grassland Investigation And Mapping Using Remote Sensing In Northern China, Liu Fuyuan, Li Znegyuan, Han Jinaguoguez, Xia Jingxin Aug 2024

Grassland Investigation And Mapping Using Remote Sensing In Northern China, Liu Fuyuan, Li Znegyuan, Han Jinaguoguez, Xia Jingxin

IGC Proceedings (1977-2023)

A large area of grassland in north China is an important resource for animal production, The grassland vegetations of north China were investigated and mapped by means of remote sensing from 1986 to 1990, We adopted various multiple information and multitime phase satellite images which were taken to allow optical and computer processing for complicated cases and difficult areas of grassland classification, The grasslands were divided into 8 different types and 50 groups according to surface features of Landsat images, ecological environment and vegetation analysis, The warm shrub and herbosa grassland, which ls distributed mainly in the rocky mountainous regions, …

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Mapping Urban Tree Canopy Using Publicly Available Satellite Data, Rosemary Mcguinness Aug 2024

Mapping Urban Tree Canopy Using Publicly Available Satellite Data, Rosemary Mcguinness

Theses and Dissertations

This project addresses the need for accessible, cost-effective tools for quantifying spatial and temporal changes in tree canopy cover in urban areas. Urban tree canopy provides a wide range of ecosystem services, including lowering air temperatures, reducing pollution, and mitigating stormwater runoff. Cities around the world have placed the expansion of their urban forests at the center of their sustainability goals. Consistent and timely data on urban tree canopy is essential for urban greening initiatives to succeed. Existing methods of accessing information about urban tree canopy are highly technical, costly, and labor-intensive, while the freely available source of tree canopy …

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Remotely Sensed Early Warning Of Algal Blooms In An Eastern Nebraska Reservoir: A Comparison Of Temporal And Spatial Indicators, Mercy Kipenda Aug 2024

Remotely Sensed Early Warning Of Algal Blooms In An Eastern Nebraska Reservoir: A Comparison Of Temporal And Spatial Indicators, Mercy Kipenda

School of Natural Resources: Dissertations, Theses, and Student Research

Cyanobacterial harmful algal blooms (CyanoHABs) detrimentally affect human, animal, and ecosystem health. Remotely sensed early warning systems for cyanoHABs in inland lakes could contribute to more proactive water quality monitoring and help mitigate negative impacts. Advances in freely available remote sensing imagery, with finer spatial, temporal, and spectral resolutions, present new opportunities for the development and comparative analysis of methods to detect sudden deterioration in lake water quality. In this thesis, I compared and tested for temporal and spatial early warning signals of cyanoHABs in field-based and remotely sensed datasets from 2019 to 2023 in Pawnee Lake in southeast Nebraska, …

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Applications Of Artificial Intelligence On Drought Impact Monitoring And Assessment, Beichen Zhang Aug 2024

Applications Of Artificial Intelligence On Drought Impact Monitoring And Assessment, Beichen Zhang

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Drought, a prevalent and consequential natural disaster, poses widespread, indirect challenges across environmental and societal dimensions. Despite considerable focus on monitoring meteorological and hydrological drought and studying their characteristics, there is a gap in assessing its multifaceted impacts, especially on societal sectors. The dissertation comprises three research essays utilizing artificial intelligence to quantitatively study multi-dimensional drought impacts. The first essay leveraged deep learning and natural language processing to predict multi-dimensional drought impacts from textual datasets, including social media, news media, and citizen scientist reports. The findings demonstrate superior performance over traditional methods and unveil the spatial and temporal heterogeneity of …

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Fishing Vessel Detection In Exclusive Economic Zones From Low Earth Orbit Satellites With Power And Computational Constraints, Kyler E. Nelson Aug 2024

Fishing Vessel Detection In Exclusive Economic Zones From Low Earth Orbit Satellites With Power And Computational Constraints, Kyler E. Nelson

All Graduate Theses and Dissertations, Fall 2023 to Present

Illegal fishing activities pose a significant threat to the sustainability of marine ecosystems and the economies and societies which rely on them. Detection of fishing vessels engaging in illegal activity is difficult, as many ships engaging in such activity actively avoid detection through radio systems used for maritime traffic monitoring. Satellite imagery provides a promising means for detecting fishing vessels, though designing an effective system is difficult due to limited availability of labeled image datasets of fishing vessels. This research proposes a system to detect illegal fishing activity through the use of a low-power ship detection satellite and proposes a …

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Deep Space Observations Of Conditionally Averaged Global Reflectance Patterns, Alexander Kostinski, Alexander Marshak, Tamás Várnai Jul 2024

Deep Space Observations Of Conditionally Averaged Global Reflectance Patterns, Alexander Kostinski, Alexander Marshak, Tamás Várnai

Michigan Tech Publications

The Deep Space Climate Observatory (DSCOVR) spacecraft drifts about the Lagrangian point ≈ 1.4 − 1.6 × 106 km from Earth, where its Earth Polychromatic Imaging Camera (EPIC) observes the entire sunlit face of Earth every 1–2 h. In an attempt to detect “signals,” i.e., longer-term changes and semi-permanent features such as the ever-present ocean glitter, while suppressing geographic “noise,” in this study, we introduce temporally and conditionally averaged reflectance images, performed on a fixed grid of pixels and uniquely suited to the DSCOVR/EPIC observational circumstances. The resulting images (maps), averaged in time over months and conditioned on surface/cover type …

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A Comparative Analysis Of Openet For Evaluating Evapotranspiration In California Almond Orchards, Kyle Knipper, Martha Anderson, Nicholas Bambach, Forrest Melton, Zac Ellis, Yun Yang, John Volk, Andrew J. Mcelrone, William Kustas, Matthew Roby, Will Carrara, Sebastian Castro, Ayse Kilic, Joshua B. Fisher, Anderson Ruhoff, Gabriel B. Senay, Charles Morton, Sebastian Saa, Richard G. Allen Jul 2024

A Comparative Analysis Of Openet For Evaluating Evapotranspiration In California Almond Orchards, Kyle Knipper, Martha Anderson, Nicholas Bambach, Forrest Melton, Zac Ellis, Yun Yang, John Volk, Andrew J. Mcelrone, William Kustas, Matthew Roby, Will Carrara, Sebastian Castro, Ayse Kilic, Joshua B. Fisher, Anderson Ruhoff, Gabriel B. Senay, Charles Morton, Sebastian Saa, Richard G. Allen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in …

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Integrating Satellite Images And Species-Based Vegetation Maps To Manage Native Grasslands, M Hall-Beyer, Q.H. J. Gwyn Jun 2024

Integrating Satellite Images And Species-Based Vegetation Maps To Manage Native Grasslands, M Hall-Beyer, Q.H. J. Gwyn

IGC Proceedings (1977-2023)

Satellite image mapping of grasslands is problematic when species diversity occurs at a sub-pixel scale. We propose a method, called melody classification, to map ground cover units that group several spectral classes (colours). Melodies are defined as the normalized expected frequencies of each class within the ground cover unit. Starting from an unsupervised classification, an image is created showing the probability of finding each spectral class in the vicinity of each pixel. Each pixel is classified by comparing the melody in its neighbourhood with that of each ground cover unit. Accuracies are greatly enhanced over those of supervised classification. Melody …

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The Ecosystem As Super-Organ/Ism, Revisited: Scaling Hydraulics To Forests Under Climate Change, Jeffrey D. Wood, Matteo Detto, Marvin Browne, Nathan J.B. Kraft, Alexandra G. Konings, Joshua B. Fisher, Gregory R. Quetin, Anna T. Trugman, Troy S. Magney, Camila D. Medeiros, Nidhi Vinod, Thomas N. Buckley, Lawren Sack Jun 2024

The Ecosystem As Super-Organ/Ism, Revisited: Scaling Hydraulics To Forests Under Climate Change, Jeffrey D. Wood, Matteo Detto, Marvin Browne, Nathan J.B. Kraft, Alexandra G. Konings, Joshua B. Fisher, Gregory R. Quetin, Anna T. Trugman, Troy S. Magney, Camila D. Medeiros, Nidhi Vinod, Thomas N. Buckley, Lawren Sack

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth's ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals, or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for …

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From Pixels To Plants: Remote Sensing Of California Invasive Plants, Kenneth Rangel May 2024

From Pixels To Plants: Remote Sensing Of California Invasive Plants, Kenneth Rangel

Master's Projects and Capstones

Invasive plants cause significant impacts to ecosystems, the economy, and human health. California has experienced significant plant invasions and is well suited to future invasion because of its Mediterranean climate and human disturbance. Eradication or control of invasive plant species requires a detailed understanding of their spatial distribution, which typically involves on the ground surveys that can be expensive or inconsistent. Remote sensing offers a potential alternative or supplement to in-person invasive plant mapping. This study performed a comparative analysis of 41 remote sensing studies that mapped the distribution of California invasive plants. I found that while high spectral resolution …

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Agroecology And Soil Stewardship: Values And Techniques Of Smallholder Farmers In Bernalillo County, Stephanie Olivas May 2024

Agroecology And Soil Stewardship: Values And Techniques Of Smallholder Farmers In Bernalillo County, Stephanie Olivas

Geography ETDs

Agrarian movements around the world use agroecology to build sovereignty and steward dynamic ecosystems. Research has shown that agroecological farmers steward more resilient crops, more resilient soil biomes, and greater biodiversity than conventional agriculture. GIS and remote sensing offer many tools to detect the impacts of these farmers on the environment, but it is less clear how such technologies fit into agroecological goals. This study asks: what values, experiences and knowledge do smallholder producers in Bernalillo County embody in their soil stewardship practices? Also, what experience or knowledge do smallholder producers in Bernalillo County have about remote sensing, and would …

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Triggers Of Rapid Change In Glacier Dynamics, Jukes Liu May 2024

Triggers Of Rapid Change In Glacier Dynamics, Jukes Liu

Boise State University Theses and Dissertations

The loss of land ice has become the greatest contributor to global mean sea level rise since 2006 (Oppenheimer et al., 2019). Glaciers have contributed ∼20% to global sea level rise over the last ∼2 decades (Oppenheimer et al., 2019; Hugonnet et al., 2021), while the ice sheets have contributed 33% (Oppenheimer et al., 2019). For the outlet glaciers and ice streams that drain the Greenland and Antarctic ice sheets, changes to their dynamics (flow speed, thickness, and length) dominate their present and future mass loss (King et al., 2020; Diener et al., 2021). One of the main limitations in …

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Microwave Emission Model Parameter Tuning For Surface Soil Moisture Retrieval Using Uav-Mounted Dual Polarization L-Band Radiometer, Santiago Hoyos Echeverri May 2024

Microwave Emission Model Parameter Tuning For Surface Soil Moisture Retrieval Using Uav-Mounted Dual Polarization L-Band Radiometer, Santiago Hoyos Echeverri

Open Access Theses & Dissertations

Surface soil moisture retrieval from L-band brightness temperature has been developed for the past decades due to multiple beneficial characteristics of 1-2 GHz frequency bands for remote sensing of the environment. Numerous microwave emission models have been proposed for tower and satellite-based operations with successful retrieval of surface soil moisture and vegetation water content. As a result of the development of cost-effective and low-mass microwave L-band radiometers such as the Portable L-band Radiometer (PoLRa), surface soil moisture surveying traditionally developed by satellite missions SMOS and SMAP can now be developed at local scales, bringing these operations to commercial small unmanned …

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Classification Of Remote Sensing Image Data Using Rsscn-7 Dataset, Satya Priya Challa May 2024

Classification Of Remote Sensing Image Data Using Rsscn-7 Dataset, Satya Priya Challa

Electronic Theses, Projects, and Dissertations

A novel technique for remote sensing image scene classification is employed using the Compact Vision Transformer (CVT) architecture. This model strengthens the power of deep learning and self-attention algorithms to significantly intensify the accuracy and efficiency of scene classification in remote sensing imagery. Through extensive training and evaluation of the RSSCNN7 dataset, our CVT-based model has achieved an impressive accuracy rate of 87.46% on the original dataset. This remarkable result underscores the prospect of CVT models in the domain of remote sensing and underscores their applicability in real-world scenarios. Our report furnishes an elaborate account of the model's architecture, training …

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Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos Apr 2024

Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos

Institute for ECHO Articles and Research

The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …

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Automated Glacier Classification In High Mountain Asia Using Machine Learning And A Random Forest Classifier, Victoria Elizabeth Halvorson Apr 2024

Automated Glacier Classification In High Mountain Asia Using Machine Learning And A Random Forest Classifier, Victoria Elizabeth Halvorson

Dartmouth College Master’s Theses

High Mountain Asia (HMA) is home to the largest mass of glaciers and ice outside the north and south polar regions. HMA glaciers are projected to experience accelerated mass loss from higher greenhouse gas emissions through the end of the century. Many studies of glacier mass balance and mass loss in HMA obtain glacier area from the Randolph Glacier Inventory (RGI). However, the RGI is designed to show glacier area across the world that is accurate to the year 2000 and, as a result, is not an accurate representation of the current state of glacier area in HMA. Additionally, glacier …

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